Brief Substance Abuse Treatment with Urban Adolescents: A Translational Research Study

Transcription

1 Journal of Child & Adolescent Substance Abuse, 18: , 2009 Copyright # Taylor & Francis Group, LLC ISSN: X print= online DOI: / Brief Substance Abuse Treatment with Urban Adolescents: A Translational Research Study MICHAEL J. MASON and MICHAEL A. POSNER Villanova University, Philadelphia, PA, USA The purpose of this translational research study was to test a brief, manualized adolescent substance abuse treatment protocol s effects in an urban community setting compared to a sample in an experimental study from which the treatment was first employed. One hundred two adolescents who were treated with a manualized protocol of five sessions of Motivational Enhancement Therapy=Cognitive Behavioral Therapy (MET=CBT-5) were followed for six months and outcomes were analyzed against a comparison sample (n ¼ 102). Both groups were treated with (MET=CBT-5). The community setting group showed reduced alcohol use relative to the comparison group at six months using unadjusted measures and at three and six months using propensity score analyses to adjust for the differences in baseline characteristics of the two groups. These findings support using brief, manualized treatments for diverse, urban adolescents in outpatient community settings. KEYWORDS brief treatment, case-mix adjustment, manualized reatment, propensity score analysis, translational study, urban adolescents This research was supported by funding from the Substance Abuse and Mental Health Service Administration, Center for Substance Abuse Treatment, grant no. TI The content of this article does not necessarily reflect the views or policies of the U.S. government. The primary author would like to acknowledge the work of the Georgetown University Adolescent Health Program staff, Sherrine Brown, MSW, Michelle Wilson, MA, and Sarah Brooks, MS, for their clinical, research, and administrative contributions. Address correspondence to Michael J. Mason, Villanova University, Department of Education & Human Services, St. Augustine Center, 800 Lancaster Ave., Villanova, PA 19010, USA. [email protected] 193

2 194 M. J. Mason and M. A. Posner INTRODUCTION Recent research provides broad consensus for the negative consequences of underage drinking including a range of physical, academic, and social problems (NIAAA, 2000; Steinman & Schulenberg, 2003; Donovan, 2004; Johnston, O Malley, Bachman, & Schulenberg, 2004). Annual surveys conducted in the United States have shown that although overall adolescent substance use has slightly decreased in the past few years, binge drinking among year-olds has recently increased, with more than 20% of this age group reporting having five or more drinks in a row within the past two weeks (Johnston et al., 2004). Due to persistent problematic drinking, the need continues to grow for effective and efficient adolescent substance abuse treatment. In 2005, 2.1 million youths in the United States aged 12 to 17 (8.3 percent of this population) needed treatment for an illicit drug or alcohol use problem. Of this group, only 181,000 youths received treatment at a specialty facility (8.6%), leaving more than 1.9 million youths who needed treatment for a substance use problem but did not receive it at a specialty facility (Office of Applied Studies, 2005). Research has shown that urban youths are particularly vulnerable to early use and future abuse of illicit drugs and alcohol (Wright, 2004). Individuals who live in metropolitan areas, regardless of race or gender, are more likely than those in non-metropolitan areas to have used an illicit drug as well as alcohol during the past year (Office of Applied Studies, 2005; Wright, 2004). African-American youths have consistently reported less alcohol use than their non-hispanic white counterparts. However, rates of heavy and problem drinking among African-American adults, especially males, are higher than for non-hispanic whites (Caetano & Clark, 1998). Studies also show that, for any given level of alcohol consumption, ethnic minority populations experience more negative health and social consequences of drinking than whites, including unemployment, poor education outcomes, and alcohol-related legal problems (Boyd, Phillips, & Dorsey, 2003). In many studies, minority patients enter treatment with more characteristics that predict lower rates of success compared with whites, including lower income, less education, more extensive family histories of alcoholism, more co-occurring drug abuse, and poorer physical health (Le Fauve, Lowman, Litten, & Mattson, 2003). But even with poorer odds of success at the beginning of treatment, minority patients often appear to be as successful as whites when followed for a year or more after treatment. A promising development to address the unmet need of adolescent substance abuse is the growing body of evidence-based treatments that have emerged in the past ten years (Dennis et al., 2000; Williams, Chang, & Addiction Centre Research Group, 2000; Winters, 1999). These treatments have recently been studied in national trials, yielding important results for patients,

3 Brief Substance Abuse Treatment 195 practitioners, and researchers. Specifically, these manualized treatment protocols reduced substance use and increased abstinence across varying types of treatment modalities. Unfortunately, the dissemination of the findings from these highly controlled efficacy trials has been slow to reach community settings where these treatments could benefit many low-resource and underserved adolescents (Griffin, Botivin, Scheier, Epstein, & Doyle, 2002; Wechsberg, Zule, Riehman, Luseno, & Lam, 2007). Coupled with the slow dissemination of evidence-based treatments is the lack of rigor in many of the research designs that are conducted in community settings. When a community program has access to an effective treatment protocol, it is often studied through a simple pre- and posttest design, without a comparison group. When comparison groups are used, statistical procedures are typically not employed that would control for differences between groups on important pretreatment risk characteristics such as treatment history, psychiatric status, and substance use severity (Morral, McCaffrey, & Ridgeway, 2004). With pretreatment characteristics being potentially confounding, it is critical to utilize effective statistical methods to test and then adjust for group differences in order to accurately assess treatment effects between groups. Predicated upon utilizing statistical modeling to control for group differences, we were interested in answering the following question: How do the promising outcomes seen in randomized trials of manualized treatments translate into an urban community treatment setting? METHODS One hundred and two (102) adolescents enrolled in a brief, manualized substance abuse treatment program between January 2004 and September Participating adolescents were 44% white, 36% African American, 9% of Hispanic origin, and 11% were of mixed or other race or ethnicity. Mean age was 16 years and the majority (80%) were male. The Institutional Review Board of the first author s university approved this study and all subjects and their legal guardians were fully informed and provided consent to participate. Project collaboration among departments of psychiatry and pediatrics at a mid-atlantic medical center provided a unique urban healthcare-based sample where patients were primarily referred by health care providers, followed by schools, and the juvenile justice system. A university department of psychiatry s outpatient treatment program served as the study site. Participants met minimum substance abuse criteria, primarily for cannabis and then alcohol abuse, and were referred for treatment based upon problems related to substance abuse. Inclusion criteria were participants aged 13 to 20; had used marijuana in the past 90 days; met one or more criteria of dependence or abuse; had parental or guardian consent to participate if younger than 18 years old; and were appropriate for outpatient treatment (American Society of Addiction Medicine, 1996). Participants were excluded if they were not

4 196 M. J. Mason and M. A. Posner appropriate for outpatient treatment, and had a medical, psychological, or language condition that would preclude full participation in the treatment. The comparison group data for the present study were drawn from a U.S. Substance Abuse and Mental Health Services Administration funded, randomized trial testing adolescent substance abuse treatment with 600 participating youths. The study was named the Cannabis Youth Treatment (CYT) Study (See Dennis et al., 2004, for complete details of the study), and was designed to test the clinical and cost-effectiveness of five short-term treatment interventions for adolescents with cannabis use disorders. The CYT study was designed to be generalized to adolescents who present at publicly funded outpatient treatment settings. Toward this end nearly half (49%) of this group met criteria for other substance use disorders, including alcohol use, as well as co-occurring psychiatric disorders. Participants were included if they were between the ages of 12 and 18, had used marijuana in the past 90 days, and met one or more criteria of dependence or abuse, and were appropriate for outpatient treatment (American Society of Addiction Medicine, 1996). Exclusion criteria was reported use of alcohol and other drugs more than 45 and 13 days respectively within the past 90 days prior to intake, and medical, psychological, or language condition that would preclude full participation in the treatment. Sample characteristics for both the community and comparison groups are detailed in Table 1. Study Design The study was a translational research investigation of a manualized treatment to compare outcomes of a controlled trial against a community-based treatment setting. For the community group the design consisted of baseline, three- and six-month follow-up interviews. The comparison group followed participants up to twelve months post-treatment. Based on budgetary and staffing constraints, the community group was followed for six months. Comparison group participants were randomly assigned to their manualized treatment condition. Follow-up rates for the comparison study were at least 90% across all follow-up interviews. Follow-up rates were 82.7% at month three and 76.9% at month six for the community-based sample. All patients received incentives, from $15 to $25 depending on which interview wave, to participate in the follow-up interviews, which lasted less than one hour. For the community and comparison groups, all assessments were conducted by trained and certified interviewers. All interviewers met a rigorous training protocol that included at least six weeks of training, audiotape review by national experts, written critiques, and ongoing supervision to ensure the collection of high-quality data with each interview. Also for both conditions, the therapists were similar in educational background, received the same type of rigorous six weeks of pre-treatment training,

5 Brief Substance Abuse Treatment 197 TABLE 1 Baseline Sample Characteristics by Treatment Condition and Weighted Propensity Score (n ¼ 204) Baseline Propensity score weighted Variable Community Comparison Community Comparison Female Race Black Hispanic White Other Age < þ Recent Psychological Problems ADHD Disorder Major Depressive Disorder General Anxiety Disorder Traumatic Stress Disorder Suicidal Problems Prior Substance Abuse History Substance Use Severity Low Moderate High Arrested Note: Numbers represent percent. p-value for baseline data: p <.01; p <.001. All p-values >.05 for post-ps. For results c-stat for PS model ¼ including audiotape and case note review, targeted readings, videotape training, and ongoing clinical supervision throughout the study. For the community study, the therapists were graduate students in social work and mental health counseling master s degree programs. In the comparison group 20% had doctorates, 50% had masters, and 30% had bachelor degrees. The community study followed the comparison group training protocol and had supervisors initially trained by the same trainers. The primary difference between the two conditions was that the community setting therapists received clinical supervision within the context of a typical community treatment program setting. That is, they did not receive specific fidelity checks or videotape analysis of sessions to assess a standard level of proficiency; rather they received supervision within a treatment team meeting context. This type of community-based supervision is more typical within the treatment field and thus provides a realistic translational setting to compare against the controlled trial. Both community and comparison group therapists were evaluated by treatment satisfaction measures completed at the end of session 2 of 5 by the adolescent. In particular, items that were most indicative of the MET=CBT

6 198 M. J. Mason and M. A. Posner treatment e.g., Asked client s opinion about problem, Respected client were closely monitored. All adolescents who enrolled in the community treatment program completed the five-session treatment, thereby eliminating selective participation bias. Assessments Substance use involvement and mental health was measured using the Global Appraisal of Individual Needs (GAIN) (Dennis, 1999). The GAIN is a standardized clinical assessment that has been normed on adolescents and adults and documents participant-reported problems associated with the use, abuse, and dependence on drugs and alcohol. The core indexes have Cronbach alphas over 0.85 to 0.90, items generally have retest reliability over 0.70, and self-reports were consistent with urine tests (kappa over 0.70) (Dennis, 1999). The community sample follow-up data were collected using the Substance Abuse and Mental Health Services Administration (SAMHSA, 2005) National Outcome Measure for Substance Use and Mental Health. It is a thirty-item measure of substance use and mental and physical health problems and is modified from the Addition Severity Index (McLellan et al., 1992). As can be seen by the resemblance of items for each measure of alcohol use, the item differences appear to be negligible and therefore provide sufficient confidence to compare group outcomes based on these items. Comparison Group Follow-up Item: During the past 90 days, on how many days have you used any kind of alcohol? Community Group Follow-up Item: During the past 30 days, how many days have you used any alcohol? Two comparison group variables were combined to estimate past 30-day alcohol use: number of days of alcohol use in the past 90 days and recency of alcohol use. The recency variable was structured as 0 ¼ never, 1 ¼ over 1 year ago, 2 ¼ 4 12 months ago, 3 ¼ 1 3 months ago, 4 ¼ 1 4 weeks ago, 5 ¼ 3 7 days ago, and 6 ¼ 1 2 days ago. If the recency variable indicated that the subject used alcohol within the past 30 days, the number of days of alcohol use in the past 90 days was divided by three to calculate the number of days of alcohol use in the past 30 days. Treatment Program Model Approximately five years ago the U.S. Substance Abuse and Mental Health Services Administration, Center for Substance Abuse Treatment, conducted the largest randomized trial to date testing adolescent substance abuse treatment with 600 participating youths. The study was named the Cannabis Youth Treatment (CYT) study (see Dennis et al., 2004, for complete details

7 Brief Substance Abuse Treatment 199 of the study), and was designed to test the clinical and cost-effectiveness of five short-term treatment interventions for adolescents with cannabis use disorders. Overall, the clinical outcomes were positive and relatively similar across sites and conditions; however, after controlling for initial severity, the most cost-effective intervention was Motivational Enhancement Therapy= Cognitive Behavioral Therapy-5 (MET=CBT-5) (Sample & Kadden, 2001). The MET=CBT-5 is used with adolescents who are abusing and dependent on various substances including alcohol, and was not conceptualized as only a cannabis treatment but as a treatment that has utility across a spectrum of substances (Sample & Kadden, 2001). MET=CBT-5 treatment is a brief approach that consists of two individual motivational enhancement therapy (MET) sessions, followed by three group cognitive behavioral therapy (CBT) sessions. The treatment is based upon Miller and Rollnick s (2002) Motivational Interviewing model. The two initial individual MET sessions are primarily intended to elicit adolescents own motivation to address their drug=alcohol use and to prepare them for the group sessions, with an introduction to functional analysis and the concept of triggers. The purpose of the three group sessions is to assist clients in the development of skills useful for stopping or reducing drug and alcohol use. Table 2 provides an overview of the MET=CBT-5 treatment structure and session goals. As displayed in Table 2, treatment is provided in both individual and group modality; however, the treatment can be conducted entirely in an individual modality if groups cannot be formed. For the community group, this was the case with all treatment being delivered in an individual modality, whereas the comparison group treatment was conducted in two sessions of individual and three sessions of group treatment. As can be seen by the approach for each session, the treatment moves from MET to TABLE 2 Motivational Enhancement Therapy and Cognitive Behavioral Therapy: Five Sessions Treatment Structure Session Modality Duration Approach Topics 1 Individual 60 min. Motivational Rapport & motivation building; Enhancement Therapy Review of personalized feedback report 2 Individual 60 min. Motivational Goal setting; Introduction to Enhancement Therapy functional analysis; Preparation for group sessions 3 Group or Individual min. Cognitive Behavioral Substance refusal skills, with role-play practice exercises 4 Group or Individual 5 Group or Individual Therapy min. Cognitive Enhancing social support network; Behavioral Increasing pleasant activities Therapy min. Cognitive Coping with unanticipated high-risk Behavioral situations and relapses Therapy

8 200 M. J. Mason and M. A. Posner CBT; however, the Motivational Interviewing spirit, i.e., the style and approach of the way the therapist interacts with clients, is expected to be present in all five sessions and serve as the clinical and theoretical guide, even when utilizing the CBT approaches. Outcomes Alcohol use is the primary outcome of interest. Days of alcohol use was measured at baseline, and at the three- and six-month follow-ups. It is acknowledged that the comparison group trial specifically targeted cannabis users and, as expected, we found significant differences at baseline between the two groups past 90 days of marijuana use (community group mean [SD] ¼ 16.8 [3.2], and comparison group mean [SD] ¼ 31.2 [2.6], with p ¼.001) and therefore confounded comparisons. Due to this difference, marijuana use was not used as an outcome in this analysis. In contrast, the baseline past 90 days of alcohol use for both groups was acceptable (community group mean [SD] ¼ 6.8 [1.6], and comparison group mean [SD] ¼ 7.1 [1.5], with an independent t-test revealing, t ¼ 192, 184 df, p ¼.84). Statistical Analysis All analyses were done using SAS 9.1 (SAS Institute, Inc., Cary, NC). Twosided p-values of 5% were considered statistically significant. The outcome variable used in analyses was decrease in alcohol consumption in the past thirty days from baseline to follow-up. This variable was calculated separately for each of the two follow-up time periods, three months and six months. Independent predictor variables were used in regression models and in both stages of the propensity score models. Demographic variables included race (black, white, Hispanic, and mixed=other), age (<14, 15 16, 17þ), and gender. Psychological variables were dichotomous indicators of whether these events occurred in the recent past and included psychological problems, major depressive disorder, general anxiety disorder, ADHD disorder, prior substance abuse treatment, traumatic stress disorder, and suicidal problems. Substance severity (trichotomized due to the observed U-shaped relationship as low ¼ no use and use; moderate ¼ abuse; high ¼ dependence and physiological dependence) and whether they had been arrested recently were also used. Multiple regressions were performed to estimate the effect that the manualized treatment had on alcohol use. All demographic and psychological variables were included as covariates. Propensity score analysis was performed using weighting by the inverse of the propensity score. Case-mix Adjustment Propensity score analysis was employed to deal with potential selection bias between the treatment and control group data (Rosenbaum & Rubin, 1983).

9 Brief Substance Abuse Treatment 201 Propensity scores represent subjects probability of being a member of the treatment condition, given a set of observed characteristics or covariates thereby removing the confounding effects between observed pretreatment factors when comparing group outcomes. The propensity score method has grown increasingly popular in public health research as a tool to address potential bias due to differences in baseline demographics (Morral et al., 2004). The first-stage model estimated the propensity of being in the community group data set (as compared to the comparison group data set) from all covariates. The propensity model is robust to develop misspecification so no variable selection procedures were employed (Drake, 1993). Analytic models used the method of weighting by the inverse of the propensity score to address potential baseline differences between these two populations (Imbens, 2000). The equality of distributions was evaluated by chi-squared tests of independence. The propensity weights used were standardized to reflect the sample size of the data and ranged from 0.53 to RESULTS Before applying propensity score weights to the analytical models, significant group differences were observed on pretreatment characteristics. For instance, four of the 12 pretreatment characteristics listed in Table 1 were significantly different at baseline. In general, the community group was more diverse, more depressed, had more low and high and less moderate substance use severity cases, and was arrested less often than the comparison group. The logistic regression model predicting membership in the community group had a c-statistic of 0.89, indicating a strong ability to predict group from the covariates and differing baseline distributions. As noted in Table 1, upon applying the propensity score adjustment to the regression models, no significant differences were found between groups. As noted, the follow-up rates were 82.7% at month three and 76.9% at month six for the communitybased sample. In order to handle the potential effects of missing data, a series of regression analyses were conducted to determine differences between baseline and follow-up sample (Little & Rubin, 1987). Covariates were created for number of assessments, whether or not the last assessment was completed, and if all assessments were complete. Further, demographic and risk behavior data were compared between completers of all assessments and non-completers. We found no significant differences revealed across all variables, providing support for the analytical plan. At baseline, the community group reported using alcohol an average of 2.5 days out of the past 30 days while the comparison group reported using alcohol an average of 2.1 days out of the past 30 days. The effects of the translation of the treatment protocol from the experimental trial to a community setting were evaluated using the change in past-30-days alcohol use from baseline to follow-up and is presented in Table 3. Each change is calculated

10 202 M. J. Mason and M. A. Posner TABLE 3 Change in Number of Days of Alcohol Use (in Past 30) from Baseline to Follow-Up at 3 and 6 Months (n ¼ 204) Factor Baseline to month 3 Baseline to month 6 Community sample 0.58 ( 1.30, 0.14) 0.58 ( 1.50, 0.34) Comparison sample 0.01 ( 0.70, 0.72) 0.72 ( 0.11, 1.55) Difference 0.59 ( 0.45, 1.63) 1.30 (0.01, 2.61) p-value Adjusted difference 0.99 ( 0.41, 2.39) 1.09 ( 0.63, 2.81) p-value Propensity score Adjusted difference 2.04 (0.84, 3.24) 1.57 (0.23, 2.91) p-value Note: Positive values represent increase in alcohol use (confidence intervals). from baseline to follow-up interview at three and six months. The unadjusted, multiple regression adjusted and propensity score adjusted differences are reported. The propensity score adjustments, which account for case-mix differences, produced significant differences at months three and six. For example, as can be seen in Table 3, alcohol use for the comparison group by month six increased 0.72 days while alcohol use for the community group by month six decreased 0.58 days, for a difference between community and comparison effects of 1.30 days of alcohol use in the past 30 days (p ¼ 0.04). The propensity score adjustment, which accounts for case-mix differences, showed a significant difference in reduction in average days of alcohol use for the community group compared to the comparison group at months three and six (2 and 1.6 fewer days per month, respectively). DISCUSSION The manualized substance abuse treatment had significant effects on reducing adolescent alcohol use in the community group when weighed against the comparison group, providing support for the translation of the protocol from experimental conditions to a community setting. Both unadjusted and propensity score adjusted analyses showed the non-equivalency of the two groups at six-month follow-up, where maximum treatment effect was observed. Three-month follow-up also showed statistically significant efficacy for the propensity score adjusted analysis. Case-mix adjustment increases confidence in the findings of differences between groups as it protects against selection bias between groups. Reduction in underage alcohol use, even two days per month, serves as a significant protective factor for adolescents. Recent research has shown that when youths drink they tend to drink intensively, often consuming four to five drinks at one time (Bonomo, Bowes, Coffey, Carlin, & Patton, 2004). It is interesting that both groups receiving the same treatment protocol had significantly different outcomes. The case-mix adjustment controlled for

11 Brief Substance Abuse Treatment 203 pretreatment risk characteristics as explanations for differences. Perhaps the differences could be attributed to a combination of the factors influencing patients entry into treatment: family issues, school, and=or legal problems. One difference to consider is the source of referrals. The community group was largely referred from health care providers (37%) and much less from the juvenile justice system (14%), whereas the comparison group was primarily referred by the juvenile justice system (51%). National data show that most adolescents are referred to treatment from the legal system (51%) with only 5% being referred by health care providers (SAMHSA, 2006). These differences between the community and comparison group referral sources are likely to account for the differences in arrest rates between the comparison (47%) and community group (18%), and although these differences were addressed through the propensity score model, identifying groups that are equal on the baseline covariates would be a more ideal study design. It is unknown whether source of referral could make such a strong effect on outcomes, or whether these differences are a function of the comparison groups involvement with the legal system and the associated correlated risk factors. An important finding from this study was the effectiveness of using a research-based protocol successfully in a diverse, urban, community setting. The community group consisted of four times the number of African- American youths compared to the comparison condition (36 to 9%) and thus represents a positive finding in the dissemination of randomized trial protocols into more diverse community-based settings. These are important data due to the limited number of African Americans participating in high-quality, evidence-based treatment (Wechsberg et al., 2007). Several limitations need to be considered in interpreting these findings. First, limitations of this study rest on follow-up interviews only extending six months and therefore limiting the understanding of the treatment s longerterm effects on outcomes. The findings from this study underscore the need for more research on longitudinal studies beyond six months. Another limitation of this study was the self-reporting of adolescents substance use. However, most studies show a relatively high level of self-reporting reliability regarding drug use with alpha coefficients ranging between 0.80 and 0.95 (Anglin, Hser, & Chou, 1993). If biases exist in our sample it should affect only conclusions about outcome differences in the case of differential bias between groups. We have no reason to suspect that such biases vary by treatment condition, however propensity scores address differences in observed covariates, but do not adjust for unobserved covariates that may also impact the selection process and be correlated with the outcome. Finally, we acknowledge the potential methodological concern by using different follow-up measures; this is less than ideal. However, the critical outcome item (alcohol use) was compared and deemed to be essentially the same. Further, the count of days used alcohol is a relatively straightforward item and is less likely to be misconstrued.

12 204 M. J. Mason and M. A. Posner In conclusion, this brief intervention appeared to have positive effects on alcohol use by urban adolescents in a community setting for up to six months past treatment, and can be considered successful in this regard. Future research should continue to focus on the dissemination of efficacious treatments into community settings and simultaneously test differing levels of treatment intensity based upon patient needs. These types of rigorous studies would continue to build the evidence base for understanding service delivery according to best practices in order to improve treatment quality and patient outcomes. REFERENCES American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Text revision. Washington, DC: Author. American Society for Addiction Medicine. (1996). Patient placement criteria for the treatment of psychoactive substance disorders (2nd ed.). Chevy Chase, MD: Author. Anglin, M., Hser, Y., & Chou, C. (1993). Reliability and validity of retrospective behavioral self-report by narcotics addicts. Evaluation Review, 17(1), Bonomo, Y. A., Bowes, G., Coffey, C., Carlin, J. B., & Patton, G. C. (2004). Teenage drinking and the onset of alcohol dependence: A cohort study over seven years. Addiction, 99(12), Boyd, M. R., Phillips, K., & Dorsey, C. J. (2003). Alcohol and other drug disorders, comorbidity, and violence: Comparison of rural African American and Caucasian women. Archives of Psychiatric Nursing, 17, Caetano, R., & Clark, C. L. (1998). Trends in alcohol consumption among whites, blacks and Hispanics: 1984 and Journal of Studies on Alcohol, 59, Dennis, M. (1999). Global Appraisal of Individual Needs (GAIN): Administration guide for the GAIN and related measures (Version 1299). Bloomington, IL: Lighthouse. Dennis, M. L., Babor, T. F., Diamond, G., Donaldson, J., Godley, S. H., Titus, J. C., et al. (2000). The Cannabis Youth Treatment (CYT) experiment: Preliminary findings. Rockville, MD: Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration, Department of Health and Human Services. Dennis, M., Godley, S., Diamond, G., Tims, F., Babor, T., Donaldson, J., et al. (2004). The cannabis youth treatment (CYT) study: Main findings from two randomized trials. Journal of Substance Abuse Treatment, 27, Donovan, J. E. (2004). Adolescent alcohol initiation: A review of psychosocial risk factors. Journal of Adolescent Health, 35(6), 529.e7 18. Drake, C. (1993). Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics, 49, Griffin, K., Botivin, G., Scheier, L., Epstein, J., & Doyle, M. (2002). Personal competence skills, distress, and well-being as determinants of substance use in a predominantly minority urban adolescent sample. Prevention Science, 3,23 33.

13 Brief Substance Abuse Treatment 205 Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 87(3), Johnston, L. D., O Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2004). Monitoring the Future. National Survey Results on Drug Use, Volume I: Secondary School Students (NIH Pub. No ). Bethesda, MD: National Institute on Drug Abuse. Le Fauve, C. E., Lowman, C., Litten, III, R. Z., & Mattson, M. E. (2003). Introduction: National Institute on Alcohol Abuse and Alcoholism Workshop on Treatment Research Priorities and Health Disparities. Alcoholism: Clinical and Experimental Research, 27, Little, R., & Rubin, D. (1987). Statistical analysis with missing data. New York: Wiley. McLellan, A. T., Kushner, H., Metzger, D., Peters, R., Smith, I., Grissom, G., et al. (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9, Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change (2nd ed.). New York: Guilford Press. Morral, A., McCaffrey, D., & Ridgeway, G. (2004). Effectiveness of community-based treatment for substance-abusing adolescents: 12-month outcomes of youths entering Phoenix Academy or alternative probation dispositions. Psychology of Addictive Behaviors, 18(3), National Institute on Alcohol Abuse and Alcoholism (NIAAA). (2000). Alcohol involvement over the life course. In Tenth special report to the U.S. Congress on alcohol and health: Highlights from current research (pp ). Bethesda, MD: Department of Health and Human Services, NIAAA. Office of Applied Studies, Substance Abuse and Mental Health Services Administration. (2005). Results from the 2004 National Survey on Drug Use and Health: National findings (DHHS Publication No. SMA , NSDUH Series H-28). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, Sampl, S., & Kadden, R. (2001). Motivational Enhancement Therapy and Cognitive Behavioral Therapy for adolescent cannabis users: Five sessions, Cannabis Youth Treatment (CYT) Series, Volume 1. Rockville, MD: Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration. Substance Abuse and Mental Health Services Administration, Center for Mental Health Services. (2005) CMHS Uniform Reporting System output tables ( State Mental Health Measures table; Appropriateness Domain Table 4). Rockville, MD: Author. Substance Abuse and Mental Health Services Administration, Office of Applied Studies. (2006). Treatment Episode Data Set (TEDS): National Admissions to Substance Abuse Treatment Services. DASIS Series: S-33, DHHS Publication No. (SMA) Rockville, MD: Author. Steinman, K. J., & Schulenberg, J. (2003). A pattern-centered approach to evaluating substance use prevention programs. In W. Damon, S. C. Peck, & R. W. Roeser (Eds.), New directions for child and adolescent development, Vol. 101:

14 206 M. J. Mason and M. A. Posner Person-centered approaches to studying development in context (pp ). San Francisco, CA: Jossey-Bass. Wechsberg, W., Zule, W., Riehman, K., Luseno, W., & Lam, W. (2007). African American crack abusers and drug treatment initiation: Barriers and effects of pretreatment intervention. Substance Abuse Treatment, Prevention, and Policy, 2, 10. Williams, R. J., Chang, S. Y., & Addiction Centre Research Group. (2000). A comprehensive and comparative review of adolescent substance abuse treatment outcome. Clinical Psychology: Science and Practice, 7, Winters, K. C. (1999). Treating adolescents with substance use disorders: An overview of practice issues and treatment outcomes. Substance Abuse, 20, Wright, D. (2004). State estimates of substance use from the 2002 National Survey on Drug Use and Health (DHHS Publication No. SMA , NSDUH Series H-23). Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies.

15